sustainability

Article

Exploring the Nature and Antecedents of Employee EnergeticWell-Being at Work and Job Performance Profiles

Tina Peeters * , Karina Van De Voorde and Jaap Paauwe

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Citation: Peeters, T.; Van De Voorde,

K.; Paauwe, J. Exploring the Nature

and Antecedents of Employee

Energetic Well-Being at Work and Job

Performance Profiles. Sustainability

2021, 13, 7424. https://doi.org/

10.3390/su13137424

Academic Editors:

Pablo Ruiz-Palomino,

Rafael Morales-Sánchez and

Ricardo Martínez-Cañas

Received: 31 May 2021

Accepted: 29 June 2021

Published: 2 July 2021

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Attribution (CC BY) license (https://

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4.0/).

Human Resource Studies, School of Social and Behavioral Science, Tilburg University,5000 LE Tilburg, The Netherlands; f.c.v.d.voorde@tilburguniversity.edu (K.V.D.V.);paauwe@tilburguniversity.edu (J.P.)* Correspondence: t.peeters@tilburguniversity.edu

Abstract: While it is generally assumed that employees who feel well are also productive, researchhas shown that this is not always the case. Specifically, some employees seem to experience lowwell-being while performing, and vice versa. As employee well-being and performance are bothrequired to achieve corporate sustainability, the purpose of this research was to identify energy-related well-being/job performance profiles among 5729 employees from the Dutch division of alarge bank and identify their antecedents. Using latent profile analysis, we found five profiles: 1. lowwell-being/low performance, 2. low well-being/medium performance, 3. high well-being/mediumperformance, 4. high well-being/high performance, and 5. high well-being/top performance. Usingmultinomial regression, we found that more learning and development opportunities, more socialsupport from colleagues, more autonomy, and less role-conflict were related to the high well-beingprofiles. Second, more role clarity, more performance feedback, more autonomy, and less work-pressure were related to the high- and top-performance profiles. Finally, communication and socialsupport from the manager were found to be relatively weak antecedents of the different profiles. Thisstudy thus highlights that the job demands and resources of employees may affect their well-beingand performance.

Keywords: well-being profiles; job demands; job resources; person-centered approach; sustain-able work

1. Introduction

In pursuit of economic viability and social responsibility, a growing number of or-ganizations are striving to simultaneously address the interests and concerns of multiplestakeholders. These companies are attempting to combine the pursuit of financial gainsand social goals (protecting the environment, promoting healthy living, and ensuringemployees’ health and well-being) [1]. In this context, management and employees aretwo important internal organizational stakeholders. While the managerial perspectiveremains dominant in the field of strategic human resource management (SHRM), therehave been recent calls to more closely consider the concerns of multiple stakeholders [2](p. 31) by exploring how win-win situations that directly both benefit employees and man-agement can be achieved [3–5]. Empirical studies have demonstrated that high happinesswell-being (e.g., employees’ satisfaction with their work) and performance can go hand-in-hand [6,7]. Other studies, however, have shown that organizations striving to improvetheir performance may also increase work intensity, which (unintentionally) negativelyaffects employee health well-being (e.g., exhaustion) [2,7,8]. This indicates that simultane-ously promoting happiness and health well-being, while stimulating job performance is achallenging endeavor for organizations. Unfortunately, employee happiness well-beinghas primarily been studied as means to achieve performance and health well-being inparallel with performance [9], and the possibility that happiness, health, and performanceco-occur in complex patterns has largely been neglected in SHRM literature. Hence, prior

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studies have provided limited insights into how organizations can simultaneously addressthe interests of managers and employees by creating sustainable jobs (high levels of per-formance combined with high levels of happiness and health well-being) [9]. Therefore,this article investigates how employee well-being and job performance co-occur in variouscombinations (reflecting different balances between management and employee interests).

Only recently have scholars begun to explore the co-occurrence of employee well-being experiences and performance behaviors at work by applying a person-centeredapproach. The few empirical studies following this line of enquiry have revealed thatdistinct types of employee well-being and employee well-being/performance profiles existe.g., [10–13]. For example, Tordera et al. [12] found evidence for four patterns of happinesswell-being and job performance, including a sustainable (happy-productive) and three un-sustainable patterns (e.g., unhappy-productive). However, thus far, scholars have primarilystudied job performance together with positive happiness indicators of well-being at work(e.g., life satisfaction and vigor), e.g., [10–13], while neglecting its negative health-relatedindicators (e.g., emotional exhaustion), or they have studied indicators of well-being atwork only [10–12]. Positive happiness well-being and negative health well-being, however,are seen as distinct, from a conceptual and empirical point of view [13–15]. Moreover,some working conditions, such as large workloads, benefit positive indicators of happinesswell-being (e.g., satisfaction) and job performance but are harmful for negative indicatorsof health well-being e.g., [16,17]. Therefore, we will investigate the combination of jobperformance and a positive aspect of happiness well-being (vigor) and a negative indicatorof health-related well-being (emotional exhaustion). There are three justifications for inves-tigating this particular combination. First, according to Schaufeli and Bakker [18], vigorand exhaustion can be seen as largely independent states of energetic well-being. Second,an increased proportion of employees feels exhausted at the end of the workday and lessvigorous [19]. Third, vigor and emotional exhaustion have profound implications for bothemployees (e.g., life satisfaction) and organizations (e.g., financial performance) [20–22]. Tothe best of our knowledge, scholars have not previously studied how positive and negativeindicators of well-being can be combined with job performance using a person-centeredapproach; thus, this is our first contribution.

To better understand the energetic well-being/performance profiles, we will also an-swer the call of Benitez, Peccei and Medina [13] to explore their antecedents. Specifically, wewill investigate the role that work conditions play in shaping these well-being/performanceprofiles following the job demands and resources (JD-R) model of Demerouti, et al. [23].The JD-R model assumes that an employee’s job demands and resources affect their well-being and performance outcomes [18,24]. As many scholars have studied the effects ofjob demands and resources on emotional exhaustion and engagement in isolation, wewill follow the Schaufeli and Taris [25] recommendation and study the effects of two jobdemands and seven job resources on combinations of vigor, emotional exhaustion, and jobperformance. Specifically, our second contribution is therefore to investigate whether jobdemands and resources can serve as antecedents for our well-being/performance profiles.

In conclusion, by exploring various energy-related employee well-being and jobperformance combinations, this study contributes to understanding whether differentemployee well-being job performance profiles exist. Furthermore, by investigating theantecedents of these profiles, our study may clarify what employees need for beneficialwell-being and performance outcomes. From a practical point of view, this may helporganizations to create sustainable jobs by offering the job demands and resources anemployee needs, thereby creating a win-win situation for employees and management.

1.1. Energetic Well-Being and Performance Profiles

As explained in the introduction, this study will investigate job performance andfeelings of vigor and emotional exhaustion. Although there are many ways to defineemployee job performance, we will focus in this research on its traditional form: taskperformance. “Task performance” refers to the proficiency with which a worker performs

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the tasks central to their function and includes, among other elements, the quantity andthe quality of their efforts [26,27]. Second, vigor is one of the three core elements ofengagement and is characterized by high levels of energy and mental resilience whileworking [28]. According to Schaufeli, Salanova, González-Romá, and Bakker [28], the othertwo core elements are dedication and absorption. Third, “emotional exhaustion” refers tofeeling depleted of one’s physical and emotional resources, and this is one of the three coreelements of burnout [29]. The other two core dimensions of burnout are depersonalizationand diminished personal accomplishment. Similar to vigor, emotional exhaustion is relatedto the energetic component of well-being. As such, it has been argued in the past thatvigor and exhaustion are opposite poles on the same continuum [28]. However, whiletesting their own assumption, Schaufeli, Salanova, González-Romá, and Bakker [28] foundthat vigor and exhaustion were only weakly negatively related. In later work, Schaufeliand Bakker [18] explained their finding, stating that, “Feeling emotionally drained fromone’s work ‘once a week’ does by no means exclude that in the same week one might feelbursting with energy,” (p. 294) and concluding that, instead of being mutually exclusive,these concepts should be seen as independent states. As vigor and emotional exhaustionare both work-related energetic well-being types, while one is an indicator of positiveenergy-related well-being and the other of negative energy-related well-being, we believethey are well suited for this study.

A study of well-being and job performance in conjunction is a step away from thevariable-centered approach that has been primarily adopted by scholars. Although thevariable-centered approach is useful for distilling general patterns, Hofmans et al. [30]argue that it is restrictive in its assumption that the research sample is homogenous. Asa consequence, it assumes that employees can be categorized as (un)happy, (un)healthy,or (not) performing and ignores the possibility that employees have unique combinationsof well-being experiences and job behaviors. Prior research has shown that there may bemore complex combinations of well-being and performance. For example, scholars havefound that some employees can be unhappy and unproductive, whereas others are happybut unproductive [10,12]. Furthermore, focusing exclusively upon employee well-being,others have found that different well-being indicators can be at odds with one another.Salanova, Del Líbano, Llorens, and Schaufeli [14], for example, found that employeescan feel vigorous while being unhappy; and Somers, Birnbaum, and Casal [15] identifiedemployees who felt both stressed and satisfied with their jobs. These findings are consistentwith the Bakker and Oerlemans [31] framework for subjective well-being, which indicatesthat different combinations of feelings of activation and pleasantness are present in fourstates of well-being (i.e., engagement, satisfaction, workaholism, and burnout). Finally,previous research has shown that energetic well-being and performance indicators maymutually influence one another, as employees who feel vigorous may have sufficient energyto perform well, in contrast to those who feel emotionally exhausted [32,33].

To capture these potential combinations of job performance and positive and negativeenergetic well-being, a person-centered approach is needed [30]. The person-centeredapproach is useful for identifying employee profiles that differ on the qualitative andquantitative levels [34]. As Meyer et al. [35] explain, quantitative differences occur whenemployee profiles can be distinguished based upon their relative score—for example,“high well-being/high performance” and “low well-being/low performance.” Qualitativedifferences, on the other hand, emerge as explained by Meyer, Stanley and Vandenberg [35]when the hierarchical order of the profile scores differ for certain groups, such as “lowwell-being/high performance” and “high well-being/low performance”. There is initialempirical support for the existence for employee profiles that differ on qualitative andquantitative grounds. Applying this person-centered approach, researchers [10–12] havestudied a number of the positive well-being indicators (e.g., satisfaction) in conjunctionwith job performance and found two synergetic patterns (i.e., high-high and low-low) andtwo antagonistic patterns (i.e., high-low and low-high).

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On the basis of this line of reasoning and prior empirical work, it seems plausiblethat two synergetic patterns (high well-being/high performance and low well-being/lowperformance) and two antagonistic patterns (high well-being/low performance and lowwell-being/high performance) could emerge from our data. Furthermore, additionalprofiles in which the positive well-being indicators and negative well-being indicators areat odds with one another could also be expected (e.g., low vigor-high performance-highemotional exhaustion, high vigor-low performance-low emotional exhaustion). However,as this is, to the best of our knowledge, the first to study positive well-being indicators(vigor), negative well-being indicators, and performance (task performance) in conjunction,we make no a priori predictions about the profiles we will identify within the data orhow many will emerge. This decision to make no predictions is consistent with previousresearch applying a person-centered approach, e.g., [36,37] and fits the inductive nature ofthis approach. Therefore, our first research question is as follows:

Research question 1: Are there distinct employee well-being profiles (vigor and emotional exhaus-tion) and performance profiles that vary quantitatively (level) and qualitatively (shape)?

1.2. Job Resources and Profiles

This section will discuss how job resources can result in different well-beingperformance profiles. In the literature, four types of job resources are distinguished:resources located at the organizational level (e.g., communication), the interpersonal level(e.g., social support), the way in which the job is organized (e.g., role clarity), and thetask level (e.g., autonomy) [24]. In general, the JD-R model assumes a similar path for alljob resources. First, in line with effort-recovery theory [38], it is argued that providingemployees with job resources increases their external motivation to invest effort in theirwork. Second, in line with self-determination theory [39], it is argued that employees whoreceive job resources become intrinsically motivated, as these resources allow them tofulfill their basic human needs for autonomy, relatedness, and competence. As a result,the “motivational path” of the JD-R model argues that job resources cause employees tofeel more vigorous and achieve a higher performance [24,25,40]. In addition, employeeswho possess more job resources have also been shown to perceive less strain, which makesthem less likely to experience feelings of feeling burned out [41]. While positive effectson well-being and performance can be expected, Schaufeli and Taris [25] warn that thesame job resources will have differential effects on outcomes. Therefore, based upon thework of Bakker and Demerouti [24], we will include seven types of job resources in ourstudy—namely, communication (organizational level); social support from colleaguesand the manager (interpersonal level); role clarity (organization of work); and learningopportunities, autonomy, and performance feedback (task level). It can thus be assumed,at a general level, that job resources will be related to positive well-being and performanceprofiles. However, as their exact relationship with well-being/performance profiles has yetto be explored, our second research question is as follows:

Research question 2: Do job resources (at the organizational, interpersonal, organization-of-work,and task levels) differentiate employee well-being from performance profile?

1.3. Job Demands and Profiles

This section will discuss how job demands can result in different well-being/performance profiles. In line the meta-analysis of Lee and Ashforth [42] and the modelof compensatory control [43], the JD-R model assumes that job demands have a negativeeffect on well-being. Specifically, it is argued that, in a situation of high demand (e.g., workpressure), employees try to protect their performance by increasing their effort. Althoughemployees can use various coping strategies to manage this (e.g., taking breaks), theirenergy levels become drained if their job demands are high for a prolonged period of time.As a result, the “health impairment path” of the JD-R model assumes that job demandslead to emotional exhaustion and, more generally, to burnout [18]. Although research has

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consistently found that job demands lead to emotional exhaustion [25], the proposition ofthe JD-R model that job demands are not directly related to engagement has been calledinto question. In a meta-analysis, Crawford et al. [44] showed that specific job demandsare positively and directly related to engagement, whereas others negatively affect thispositive indicator of well-being. Consequently, they argue that there are two types of jobdemand: challenging demands and hindering demands. Although both types of demandlead to increased levels of emotional exhaustion, Crawford, LePine, and Rich [44] foundthat challenging demands (e.g., work pressure) can motivate employees to perform. Inline with this, challenging demands have been found to be positively related to vigor andperformance [16,44]. In contrast, hindering demands (e.g., role conflict) are associated withnegative emotions that make employees less willing to invest energy into dealing withthem [44]. In line with this, hindering demands have been found to be negatively relatedto vigor and performance [16,44,45].

Based upon the previous, it can thus be assumed that challenging demands andhindering demands have different effects on task performance, vigor, and emotionalexhaustion. Therefore, we have included work pressure as a challenging demand androle conflict as a hindering demand in our model, in line with prior work [24,46]. As fewscholars have explored the relationships between job demands and performance, vigor,and emotional exhaustion together—much less in conjunction—our final research questionis as follows:

Research question 3: Do job demands (challenging and hindering) relate to different employeewell-being and performance profiles?

2. Materials and Methods2.1. Sample and Procedure

For this research, a pre-existing dataset was taken from a large multinational companyoperating in the financial sector. The company originated through a series of mergers in theNetherlands, after which it expanded throughout the world. As of today, it is among thelargest 30 banks worldwide and has its head office within the Netherlands. To offer a workenvironment that enhances well-being and enables employees to perform at their best, thecompany collaborates with a consultancy agency to improve the vitality of its workforcethrough 1. a self-assessment report for individual employees that provides insights intoits workers’ job demands, resources, and well-being, while offering tips and tricks forimprovement; and 2. departmental- and organizational-level reports, with insights formanagement into workers’ job demands, resources, and well-being. To generate thesereports, surveys were distributed by the consultancy firm to all employees working in theNetherlands (N = 18,230) in June 2020. In total, 8839 (31%) people filled out the survey.

Due to the sensitive topics assessed in this survey (e.g., work pressure), the surveyincluded no demographic questions. For similar reasons, we only had access to the fullyanonymized data set, which means that we cannot discuss the specific characteristics ofour sample. Instead, we will describe the characteristics of the entire population workingfor this organization in the Netherlands. This population consists of those working for thehead office (n = 8413) and the Dutch division of the bank (n = 9817). There were similarresponse rates for the head office workers (28%) and Dutch division workers (33%). Inthis population, 64% of the workers are male and 36% are female. In addition, 2.31% areaged under 25 years, 29% are 26–35 years, 28.4% are 36–45 years, 26.7% are 46–55 years and13.7% are older than 56 years. Finally, 85.5% of the employees have Dutch nationality. Asthe entire population was involved in this research, the functional areas included rangedfrom sales agents up to the CEO of the organization.

Finally, as we made use of a pre-existing dataset, we requested and acquired theapproval of our university’s ethical review board after the dataset had been acquired.Similarly, the data privacy officer of the organization granted us permission to use this datafor academic research after the primary purpose had been completed (e.g., providing self-assessment reports to employees and departments and organizational reports to managers).

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2.2. Measures

The survey offered by the consultancy firm was the “JD-R monitor.” This survey is acommercialized online survey, developed based upon the JD-R model [25], in collaborationwith Prof. Dr. Wilmar Schaufeli, a leading scholar in this area. All rights to the itemsand scales are reserved by the consultancy agency. To assess the reliability and validityof the antecedents of the well-being/performance items, we conducted a confirmatoryfactor analysis (CFA) and reliability analysis. The CFA was conducted using the packageLavaan (v.06-8) and the reliability analysis used the Psych package (v2.1.3) in R. Likeother researchers [47–49], we conducted a latent class analysis (LCA) on the item level toidentify the well-being and performance profiles. We did not conduct these analyses forthe included well-being and performance items.

Job resources: In total, seven job resources were included in this research. Theseresources can be subdivided in four categories, according to Bakker and Demerouti [24]:resources stemming from the organization at large, the way in which the work is organized,interpersonal resources, and task resources. All resources were measured using three itemseach. First, communication was included as a job resource at the organizational level. Forexample, “I am sufficiently informed about developments within my organization.” The answerscale for both variables ranged from “totally disagree” (1) to “totally agree” (5), and itappeared to be sufficiently reliable (α = 0.68). Second, one type of resource concerned theway in which the work was organized (“role clarity”). One example item is, “Do you knowexactly what is expected of you at work?” Answers were given using a Likert-scale, rangingfrom “never” (1) to “always” (5). The scale was found to be reliable (α = 0.80). Third, twotypes of resources associated with the interpersonal level were included—namely, socialsupport from colleagues and from (line) management. An example of an item for colleaguesupport is, “Can you count on your colleagues for help and support, when needed?”, and anexample for management support is, “Can you count on your line manager for help and supportwhen needed?”. The answer scales ranged from “never” (1) to “always” (5) and the reliabilityof colleague support (α = 0.76) and manager support (α = 0.87) appeared to be sufficient.Fourth and finally, three types of task-level resources were included: learning opportunities,autonomy, and performance feedback. An example of the learning opportunities scaleis, “My job offers adequate opportunities for personal growth and development.” Second, “Canyou determine the content of your work?” is an example from the autonomy scale. Third,for performance feedback, an example item is, “Does your line manager provide informationabout how well you do your job?”. The respondents were, again, invited to answer on ascale ranging from “never” (1) to “always” (5). The Cronbach alpha’s of the learningopportunities (α = 0.86), autonomy (α = 0.80), and feedback (α = 0.76) scales indicated thatthey were reliable.

Furthermore, two CFAs were conducted simultaneously for the seven job resources.First, a unidimensional model was tested to verify whether the different dimensions shouldindeed be treated as separate types of resource. The model fit indicators, specifically thechi-square (χ2), comparative fit index (CFI), root mean square error of approximation(RMSEA) and standardized root mean square residual (SRMR), indicated that the unidi-mensional model was a poor fit for the data (χ2 = 23,856 (189), CFI = 0.550, RMSEA = 0.148,SRMR = 0.107). In contrast, the hypothesized model consisting of seven dimensions didresult in a sufficient model fit (χ2 = 3688 (168), CFI = 0.933, RMSEA = 0.060, SRMR = 0.038).Therefore, all seven job resources were included as separate variables in the sequentialanalyses.

Job demands: For this research, two job demands were included from two subcate-gories. Specifically, we included the hindering-demand role conflict and the challenging-demand work pressure [16]. Both job demands were measured with three items, using aLikert scale ranging from “never” (1) to “always” (5). Role conflict was measured withitems such as, “Do you have to do things at work that you would prefer to do differently?” andwork pressure with items such as, “Do you have too much work to do?”. The scales for roleconflict (α = 0.76) work pressure (α = 0.80) appeared to be reliable. Again, we conducted

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a series of CFA for the items. First, the unidimensional model was tested again, withall the items of both demands loaded onto one factor. As this model indicated a poorfit (χ2 = 3587 (9), CFI = 0.660, RMSEA = 0.263, SRMR = 0.156), the hypothesized modelwith the two separate demands was tested instead. This model appeared to fit the data(χ2 = 144 (8), CFI = 0.987, RMSEA = 0.055, SRMR = 0.032). Consequently, role conflict andwork pressure were included in the sequential analyses.

Well-being: As explained before, both positive and negative facets of well-being wereincluded in this research. We included two items for each facet, and respondents answeredon a Likert-point scale ranging from “never” (1) to “always” (5). Vigor was included usingtwo items from the Utrecht work engagement scale [50], and an example is, “At work, I ambursting with energy.” Emotional exhaustion was included using two items from the Utrechtburnout scale of Schaufeli and Van Dierendonck [51], and an example is, “I feel burned outat work.”

Performance: For performance, we included three items that used an 11-point Likert-scale ranging from “totally dissatisfied” (1) to “totally satisfied” (11). An example of anitem is, “How would you rate the quality of your work in the past four weeks?”.

Finally, we investigated the correlations between the well-being and performanceitems. This analysis showed that the two items on similar topics (i.e., vigor, emotionalexhaustion, or performance) had correlations ranging from 0.54 to 0.71. Between thetopics, the correlations between the items ranged between −0.41 and 0.32. Therefore, itis concluded that neither the items nor their topics were redundant and different profilesmay appear when conducting an LCA on these seven items.

2.3. Analyses

To identify the relevant employee well-being/performance profiles, we used LCA.At its core, LCA assesses whether the parameters of a statistical model vary for differentunobserved subgroups [52]. This means that LCA can be used to identify typical responsepatterns for specific questions and identify sub-groups within the dataset. As such, itis typically used as an inductive approach when the number of classes is not knownbeforehand [36]. To determine the optimal number of profiles, the resulting models arecompared based upon their model fit, specifically the Akaike information criterion (AIC),Bayesian information criterion (BIC), chi-square (χ2) and the G-square (G2), and the primarygoal is to select a fitting model with the lowest number of classes [49]. For the model fitindicators used in this study, lower values indicate better performing models. Aside fromstatistical criteria, the model should also be evaluated for its theoretical and empiricalinterpretability. In line with Magidson, Vermunt, and Madura [49], we ran the LCA onthe item scores to identify meaningful response patterns in the data. For this purpose, weused the package PoLCA (v1.4.1) in R. The parameters were estimated using the maximumlikelihood method, with 1000 iterations to identify different profiles within the data basedupon a pre-set number of classes. Rerunning the analyses with different numbers of classesallowed us to identify the most suitable number of well-being and performance profiles.Sequentially, we labeled the clusters by investigating their response patterns on the itemsmeasuring vigor, exhaustion, and performance, and we assigned each respondent to theirpredicted class using the PoLCA package. A new categorical variable was thus createdfor each respondent to indicate their class membership. To verify the profiles, variousanalyses of variance (ANOVAs) and Tukey’s multiple comparison tests were performed toassess whether the mean scores of the vigor, exhaustion, and performance items variedsignificantly between the profiles.

To assess which antecedents are predictive of the resulting profiles, we conducted amultinomial regression analysis using the package Nnet (v.7.3-16). This analysis comparedthe likelihood of an employee being a member of a certain profile with a referent groupbased on a specific antecedent. To facilitate the interpretation of the results, the oddsratio (OR) was calculated. The coefficients were transformed into an OR by taking theexponential of the coefficients. The OR reflects “the change in likelihood of membership

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in a target profile versus a comparison profile associated for each unit of increase in thepredictor” [53]. Thus, an OR of 2 suggests that, with each unit-increase of a specific jobdemand or resource, the likelihood of being a member of a specific profile is two timeshigher than that of the referent profile. An OR under 1, for example 0.50, suggests thatthe likelihood of profile membership is reduced by 50% in comparison to the referentprofile [53]. Finally, it should be noted that, as in other LCA studies (e.g., [14,36,37]), noadditional control variables were used in this analysis.

3. Results3.1. Descriptives

Table 1 shows the means, standard deviations, correlations, and Cronbach alphas ofthe job demands and resources. As can be seen from this table, all job resources correlatedpositively with one another and were typically negatively correlated with the job demands.The exception to this were the correlations of work pressure with learning and development(r = 0.03, p < 0.05) and performance feedback (r = 0.05, p < 0.01). Similarly, role conflict andwork pressure appeared to be positively related with one another (r = 0.30, p < 0.01).

Table 1. Correlations and descriptives of the antecedents.

M SD 1 2 3 4 5 6 7 8 9

1. Learningopportunities 3.69 0.79 (0.86)

2. Communication 3.42 0.67 0.42 ** (0.68)3. Role clarity 3.71 0.75 0.23 ** 0.36 ** (0.80)

4. Colleagues socialsupport 3.86 0.73 0.35 ** 0.35 ** 0.34 ** (0.76)

5. Manager socialsupport 3.89 0.92 0.37 ** 0.46 ** 0.35 ** 0.47 ** (0.87)

6. Feedback 3.07 0.78 0.33 ** 0.42 ** 0.36 ** 0.47 ** 0.54 ** (0.76)7. Autonomy 3.45 0.84 0.33 ** 0.27 ** 0.09 ** 0.21 ** 0.23 ** 0.23 ** (0.80)

8. Work pressure 3.31 0.86 0.03 * −0.08 ** −0.04 ** −0.06 ** −0.08 ** 0.05 ** −0.03 ** (0.80)9. Role conflict 2.17 0.74 −0.23 ** −0.34 ** −0.29 ** −0.23 ** −0.23 ** −0.09 ** −0.08 ** 0.30 ** (0.76)

Note. M and SD represent mean and standard deviation, respectively. The Cronbach’s alpha is displayed on the diagonal. Values rangefrom 1–5 for all variables, for which 1 is the lowest score and 5 is the highest. * indicates p < 0.05. ** indicates p < 0.01.

3.2. Well-Being and Performance Profiles

As mentioned in the methods section, the number of classes was determined by aninductive approach in which the number of classes was increased until the most statisti-cally and theoretically suitable model had been found. The pre-determined number wasincreased until the fit measures—such as the BIC and AIC—no longer indicated a modelimprovement or until the model no longer converged. In Table 2, the model fit indicatorsare presented for LCA solutions up to six classes. For seven classes, the model no longerconverged. As can be seen from the model, fit indices such as the BIC and AIC favor themost complicated, six-class model, as both values are at their lowest point for this solution.However, the difference in fit indices with the five-class model is relatively small, and thesix-class model often failed to replicate the solution. Moreover, in the six-class solution, aprofile consisting of a very small sub-set (4.36% of the sample) was created and the solutionappeared to be more difficult to interpret than the five-class solution. Therefore, the five-class solution was chosen as it statistically outperformed the other solutions and providedthe most meaningful solution from both the theoretical and the empirical perspectives.

The five-class solution is shown in Figure 1. As shown there, the first class (12.1%) haslow scores for all well-being and performance items (orange line). Therefore, we labeled thisthe “low well-being/low performance” profile. The second class (19.7%) scored relativelyhighly on the well-being items, but it had the second-to-lowest performance pattern of allprofiles (gray line). This profile was sequentially labeled the “high well-being/mediumperformance” profile. The third class (21.5%) is characterized by its low scores on thewell-being items, but it scored mid-range for performance (blue line). Therefore, we

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labeled this class the “low well-being/medium performance” profile. The fourth class(30.4%) scored highly on all well-being and performance profiles (blue line) and thereforereceived the label of the “high well-being/high performance” profile. Finally, the fifthgroup (16.2%) also scored highly on all well-being items, but it had a distinctively high-performance pattern (purple line). Therefore, we labeled this class the “high well-being/topperformance” profile.

Table 2. Summary of the latent class analysis (LCA) for the different models.

2 Classes 3 Classes 4 Classes 5 Classes 6 Classes

ML −55,120 −53,350 −52,352 −51,659 −51,162P 93 140 187 234 281χ2 945,706,587 363,021,769 33,287,984 23,041,505 23,251,183

BIC 111,045 107,912 106,322 105,344 104,793AIC 110,426 106,981 105,078 103,787 102,923G2 27,040 23,501 21,504 20,119 19,125

Note. Abbreviations used are as follows: maximum likelihood (ML), parameters (P), chi-square (χ2), Akaikeinformation criterion (AIC), Bayesian information criterion (BIC), chi-square (χ2) and the G-square (AIC, BIC, χ2,and (G2).

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Figure 1. Profile plot five-class solution. To facilitate the interpretation of this figure, the items from “emotional exhaus-tion” were mirrored, so that higher scores indicate increased levels of well-being. Moreover, the performance scale ranging from 1–11 was transformed to a five-point scale.

We conducted ANOVAs to test whether the profiles that we found were distinctive. A Tukey’s mean score comparison test was conducted where the ANOVA results indi-cated that the mean scores differed significantly from one another. These analyses showed that the means of the first vigor item (Mdiff = 0.037, p = 0.851) and the second vigor item (Mdiff = 0.089, p = 0.067) were not significantly different for the “low well-being/medium performance” profile and the “low well-being/low performance” profile. Additionally, we found that, for the “high well-being/medium performance” profile and the “high well-being/high performance” profile, the means of the first (Mdiff = −0.024, p = 0.914) and sec-ond exhaustion items (Mdiff = 0.0252, p = 0.917) were not significantly different. As all other mean scores for vigor, exhaustion, and performance were found to be significantly different across the five profiles, it is concluded that it is indeed possible to distinguish five unique well-being and performance profiles, as identified in the LCA.

Finally, it should be noted that the profiles we found differed in qualitative and quan-titative terms. In terms of quantitative differences, we found three profiles that differ in their levels of all vigor, exhaustion, and performance items (i.e., the “low well-being/low performance,” “high well-being/high performance,” and “high well-being-top perfor-mance” profiles). In addition, we found two qualitative different profiles. One combined low levels of well-being with medium levels of performance (i.e., the “low well-being/me-dium performance” profile) and the other combined high levels of well-being with me-dium levels of performance (i.e., “high well-being/medium performance” profile). There-fore, the results confirm the value of using the person-centered LCA method [35].

3.3. Test of Antecedents To answer our second and third research questions, we investigated whether job de-

mands and resources were differentiated for the five profiles that we found. Specifically, we used multinomial regression to test whether seven job resources (communication, role

Figure 1. Profile plot five-class solution. To facilitate the interpretation of this figure, the items from “emotional exhaustion”were mirrored, so that higher scores indicate increased levels of well-being. Moreover, the performance scale ranging from1–11 was transformed to a five-point scale.

We conducted ANOVAs to test whether the profiles that we found were distinctive. ATukey’s mean score comparison test was conducted where the ANOVA results indicatedthat the mean scores differed significantly from one another. These analyses showed thatthe means of the first vigor item (Mdiff = 0.037, p = 0.851) and the second vigor item(Mdiff = 0.089, p = 0.067) were not significantly different for the “low well-being/mediumperformance” profile and the “low well-being/low performance” profile. Additionally,we found that, for the “high well-being/medium performance” profile and the “highwell-being/high performance” profile, the means of the first (Mdiff = −0.024, p = 0.914)and second exhaustion items (Mdiff = 0.0252, p = 0.917) were not significantly different. Asall other mean scores for vigor, exhaustion, and performance were found to be significantly

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different across the five profiles, it is concluded that it is indeed possible to distinguish fiveunique well-being and performance profiles, as identified in the LCA.

Finally, it should be noted that the profiles we found differed in qualitative andquantitative terms. In terms of quantitative differences, we found three profiles thatdiffer in their levels of all vigor, exhaustion, and performance items (i.e., the “low well-being/low performance,” “high well-being/high performance,” and “high well-being-topperformance” profiles). In addition, we found two qualitative different profiles. Onecombined low levels of well-being with medium levels of performance (i.e., the “low well-being/medium performance” profile) and the other combined high levels of well-beingwith medium levels of performance (i.e., “high well-being/medium performance” profile).Therefore, the results confirm the value of using the person-centered LCA method [35].

3.3. Test of Antecedents

To answer our second and third research questions, we investigated whether jobdemands and resources were differentiated for the five profiles that we found. Specifically,we used multinomial regression to test whether seven job resources (communication, roleclarity, social support from colleagues, social support from managers, learning opportu-nities, autonomy, and performance feedback) and two job demands (work pressure androle clarity) determined profile membership. The results of this analysis can be foundin Table 3.

Organizational-level resources: Communication appeared to do relatively little todistinguish our profiles, with two exceptions. First, employees who perceive higherlevels of communication were 1.3 to 1.5 times less likely to be in the “low well-being/lowperformance” profile compared to the other profiles. Second, communication increased thelikelihood of membership in the “high well-being/high performance” profile relative to“high well-being/medium performance” (OR = 1.22).

Organization of work: Having more role clarity appeared to increase the likelihoodof being in the high- or top-performance profile, in comparison with the low-to-mediumperformance profiles. Especially noticeable in this regard was that more role clarity madeindividuals 2.6 times more likely to be in the “high well-being/top performance” profilethan in the “low well-being/low performance” profile. Furthermore, compared with the“low well-being/low performance” profile, greater role clarity increased the likelihoodof being in one of the two medium performance profiles (“high well-being/mediumperformance” [OR = 1.36] and “low well-being/medium performance” [OR = 1.26]). Finally,for the two high-performance profiles, greater role clarity increased the likelihood ofbeing in the “high well-being/top performance” profile, compared to the “high well-being/high performance” profile (OR = 1.52). This means that, of all the profiles, employeeswith high levels of role clarity were most likely to fall into the “high well-being/topperformance” profile.

Interpersonal resources: Social support from the managers rarely appeared to bepredictive of class membership. The only exception was where this appeared to differentiatebetween the “low well-being/medium performance” profile and two other profiles. First,the likelihood of being in the “low well-being/low performance” profile decreased by0.82 for employees with higher levels of social support from their manager in comparisonto the “low well-being/medium performance” profile. Second, employees with moresocial support from their manager were more likely to be in the “high well-being/topperformance” profile, compared to the “low well-being/medium performance” profile.

Having social support from one’s colleagues appeared to be an important antecedentof the profiles, as employees with more social support from their colleagues were morelikely to be a member of the high well-being profiles than the low well-being profiles.Specifically, the ORs of the high well-being profiles ranged between 1.34 and 1.39 incomparison to the low well-being profiles. When the two low well-being and three highwell-being profiles are viewed independently, social support from colleagues appears todo little to differentiate between them.

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Table 3. Results of the multinomial regression.

Low WB-LowPerf vs. HighWB-Med Perf

Low WB-LowPerf vs. Low

WB-Med Perf

Low WB-LowPerf vs. HighWB-High Perf

Low WB-LowPerf vs. HighWB-Top Perf

High WB-MedPerf vs. Low

WB-Med Perf

High WB-MedPerf vs. HighWB-High Perf

High WB-MedPerf vs. HighWB-Top Perf

Low WB-MedPerf vs. HighWB-High Perf

Low WB-MedPerf vs. HighWB-Top Perf

High WB-HighPerf vs. HighWB-Top Perf

Coef. OR Coef. OR Coef. OR Coef. OR Coef. OR Coef. OR Coef. OR Coef. OR Coef. OR Coef. OR

Communication 0.23 ** 1.26 0.27 ** 1.31 0.42 ** 1.52 0.39 ** 1.48 0.05 1.05 0.20 * 1.22 0.17 1.18 0.15 1.16 0.12 1.13 −0.03 0.97

Role clarity 0.31 ** 1.36 0.23 ** 1.26 0.54 ** 1.72 0.96 ** 2.60 −0.08 0.92 0.23 ** 1.25 0.64 ** 1.90 0.31 ** 1.36 0.73 ** 2.07 0.42 ** 1.52

Social support (colleagues) 0.31 ** 1.36 −0.02 0.98 0.31 ** 1.36 0.29 ** 1.34 −0.33 ** 0.72 0.00 1.00 −0.02 0.98 0.33 ** 1.39 0.31 ** 1.37 −0.02 0.98

Social support (manager) −0.09 0.91 −0.20 ** 0.82 −0.10 0.91 −0.04 0.96 −0.11 0.89 −0.01 0.99 0.05 1.05 0.10 1.11 0.16 * 1.18 0.06 1.06

Learning opportunities 0.52 ** 1.68 0.16 ** 1.17 0.67 ** 1.95 0.64 ** 1.90 −0.36 ** 0.70 0.15 ** 1.16 0.13 1.13 0.51 ** 1.67 0.49 ** 1.63 −0.03 0.97

Performance feedback 0.11 1.12 0.09 1.10 0.24 ** 1.27 0.45 ** 1.57 −0.02 0.98 0.12 1.13 0.33 ** 1.40 0.14 * 1.15 0.35 ** 1.42 0.21 ** 1.24

Autonomy 0.17 * 1.19 −0.05 0.95 0.23 ** 1.26 0.32 ** 1.39 −0.22 ** 0.80 0.06 ** 1.06 0.15 ** 1.17 0.28 ** 1.33 0.37 ** 1.45 0.09 1.10

Work pressure −0.13 * 0.88 0.39 ** 1.47 0.12 1.12 0.36 ** 1.44 0.51 ** 1.67 0.24 ** 1.28 0.49 ** 1.63 −0.27 ** 0.76 −0.02 0.98 0.25 ** 1.28

Role conflict −0.69 ** 0.50 −0.06 0.94 −0.79 ** 0.45 −0.48 ** 0.62 0.63 ** 1.87 −0.10 0.90 0.21 * 1.23 −0.73 ** 0.48 −0.42 ** 0.66 0.31 ** 1.36

Note: Coef. and OR represent the multinomial logistic regression coefficient and odds ratio, respectively. The abbreviations WB refer to well-being, perf to performance, med to medium and vs. to versus.* = p < 0.05, ** = p < 0.01.

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Task resources: Learning opportunities, performance feedback, and autonomy ap-peared to be important antecedents of our profiles. First, having more learning anddevelopment opportunities increased the likelihood of an employee being in one of thethree high well-being profiles, in comparison with the low well-being profiles. Specifically,the ORs of the high well-being profiles ranged between 1.67 and 1.90, in comparison tothe “low well-being” profiles. When the two “low well-being” and three “high well-being”profiles are viewed separately, only two differences are observed. First, compared to the“low well-being/low performance” profile, employees with high learning and develop-ment opportunities were more likely to be in the “low well-being/medium performance”profile (OR = 1.17). Second, employees with higher levels of learning and developmentopportunities were 1.16 times more likely to be in the “high well-being/high performance”profile than in the “high well-being/medium performance” profile.

Second, it appeared that employees with higher levels of performance feedback weremore likely to be among the two high-to-top performance profiles than the three low-to-medium performance profiles. For example, these employees were 1.57 times morelikely to be a member of the “high well-being/top performance” profile than the “lowwell-being/low performance” profile. Furthermore, although having more performancefeedback did not appear to differentiate the three low-to-medium performance profiles,it did increase the likelihood of being in the “high well-being/top performance” profile,compared to the “high well-being/top performance” profile (OR = 1.24).

Third, having a high degree of autonomy appeared to increase the chances of beingin the three high well-being profiles. Specifically, compared to the two low well-beingprofiles, the OR of being in one of the three high well-being profiles ranged from 1.19 to1.45. Furthermore, having higher autonomy increased the likelihood of being in the high-or top-performance profile, compared to the low or medium performance. Specifically,the ORs of the high-performance profiles ranged between 1.06 and 1.45 in comparison tothe low-to-medium performance profiles. However, while more autonomy increased thelikelihood of being in the “high well-being/high performance” profile, compared to “highwell-being/medium performance,” this was only by a small degree (OR = 1.06).

Conclusion on job resources: In conclusion, it appears that job resources have distincteffects on well-being and performance profiles. In general, employees with more learningopportunities, social support from their colleagues, and autonomy are more likely to fallinto the three high well-being profiles than the two low well-being profiles. Furthermore,employees with more role clarity, performance feedback, and autonomy are more likely tobe in the two profiles characterized by high performance than in the three medium-to-lowperformance profiles. Finally, although there were some differences between the profiles,communication and social support from the manager did little to differentiate between theprofiles. However, employees who scored highly for communication had a lower chance ofbeing in the “low well-being/low performance” profile than in any of the other profiles.

Challenging demands: Work pressure appeared to be an important differentiatorfor the profiles. Most notable was that greater work-pressure made employees 1.28 to1.67 times less likely to be in the “high well-being/medium performance” profile. Fur-thermore, having more work-pressure made employees more likely to be in the “lowwell-being/medium performance” profile than in the “low well-being/low performance”profile (OR = 1.47). Additionally, exposure to high work-pressure decreased the likeli-hood of being in the “high well-being/high performance” profile, in comparison with the“low well-being/medium performance” profile (OR = 0.76). Finally, high work-pressureincreased the likelihood of being in the “high well-being/top performance” profile, com-pared to the “low well-being/low performance” (OR = 1.44) and the “high well-being/highperformance” profiles (OR = 1.28).

Hindering demand: Generally, it appeared that perceptions of high role-conflictdecreased the likelihood of being in one of the three high well-being profiles, in comparisonwith the two low well-being profiles. Specifically, the OR of being in one of the three highwell-being profiles—compared to the two low well-being profiles—ranged from 0.45 to

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0.66. Most notable was that, for individuals with high levels of role conflict, the chancesof being in the “high well-being/high performance” profile decreased by 55%. However,higher levels of role conflict increased the chances of an employee to be in the “high well-being/top performance,” in comparison with the other two well-being profiles. Specifically,role conflict increased the likelihood of being in the “high well-being/top performance”profile by 1.23, compared with the “high well-being/medium performance” profile, and by1.36 compared with the “high well-being/high performance” profile.

Conclusion of job demands: Lower levels of work pressure increased the likelihood ofan employee being a member of the “high well-being/low performance” profile. Further-more, while differences between specific profiles were found, it appeared that greater workpressure was generally associated with the two higher well-being and (top) performanceprofiles. With regards to role conflict, it was found that employees with less role-conflicthave a higher likelihood of having one of the three high well-being profiles in comparisonto the two low well-being profiles. Surprisingly, when the three high well-being profilesare viewed in turn, it seems that having greater role conflict increases the chances of beingin the “high well-being/top performance” profile.

The primary results of the analyses are also summarized in Figure 2.

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being profiles are viewed in turn, it seems that having greater role conflict increases the chances of being in the “high well-being/top performance” profile.

The primary results of the analyses are also summarized in Figure 2.

Figure 2. Summary of the results. Figure 2. Summary of the results.

4. Discussion

The aim of this study was to increase our understanding of how organizations cancreate jobs that are sustainable from the perspectives of both employee well-being andjob performance. To do so, we first used LCA to identify well-being and performanceprofiles. Five energy-related employee well-being and job performance profiles emergedfrom the data. These profiles differed on quantitative and qualitative grounds. Specifically,we found three profiles that differed quantitatively (1. low well-being/low performance,2. high well-being/high performance, and 3. high well-being/top performance) and twoprofiles that differed qualitatively (4. low well-being/medium performance and 5. highwell-being/medium performance). As each profile consisted of a substantial proportion ofour sample (between 12.1% and 30.4%), these outcomes support the existence of relevantand theoretically meaningful energy-related employee well-being/performance profiles.

Second, we explored whether two job demands and seven resources predicted well-being/performance profile membership. Most notably, having more learning opportunities,autonomy, and social support from colleagues and less role-conflict increased the likelihood

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of an employee being in one of the three high well-being profiles. Having more role clarity,performance feedback, and autonomy each increased the chances of being a member of thehigh- or top-performance profiles. Furthermore, employees with low work-pressure weretypically in the “high well-being/medium performance” profile, while employees whoperceived little communication had a higher likelihood of being in the “low well-being/lowperformance” profile than in the other profiles. In general, though, communication andsocial support from the manager did little to differentiate our profiles. In sum, thesefindings indicate that job demands and resources relate differently to employee well-being/performance profiles.

Although to the best of our knowledge, this is the first study of job performance andpositive (vigor) and negative (exhaustion) energetic well-being indicators, the profiles thatwe found are mostly consistent with previous findings on performance/well-being at workprofiles. While focusing upon performance in combination with positive well-being indica-tors, other researchers have similarly found low well-being/low performance and highwell-being/high performance profiles together with low well-being/high performance andhigh well-being/low performance profiles [10–12]. In contrast to those results, our findingsindicate that there may be an additional fifth profile for employees who show superiorjob performance over the high/high profiles. This “high well-being/top performance”profile consisted of a substantial part of our sample (16.2%). In addition, while our find-ings do not suggest qualitative differences between the positive and negative well-beingindicators, other scholars who have focused exclusively on well-being profiles have foundqualitative different patterns [14,15]. For example, Salanova, Del Líbano, Llorens, andSchaufeli [14] note that, aside from high well-being and low well-being profiles, employeeswho have high energy while working but derive no pleasure from it (e.g., workaholics)also exist. In contrast, a recent study by Benitez, Peccei, and Medina [13] only foundquantitative differences between positive and negative well-being indicators (e.g., low/lowand high/high profiles). Although vigor and emotional exhaustion were not found to behighly correlated, in line with Schaufeli, Salanova, González-Romá, and Bakker [28], thisdoes call into question the benefits of including multiple well-being indicators in a studyof well-being/performance profiles. As we only included energetic well-being indicatorsin our model, future research is needed to consider other types of well-being (e.g., stress,meaningfulness) and determine whether the inclusion of different well-being indicatorshelps to identify profiles from a qualitative perspective.

Turning to the antecedents of these five profiles, the first point to note is the generalpattern that those employees with more resources and fewer hindering demands andmore work-pressure are more likely to be in the “high well-being/high performance” and“high well-being/top performance” profiles, compared to the three low well-being andlow-to-medium performance profiles. This finding is consistent with the JD-R’s “motiva-tional path,” which argues that job resources increase employee well-being and perfor-mance [24,25,40]. Our findings are also in line with the Crawford, LePine, and Rich [44] dis-tinction between challenging demands and hindering demands. Specifically, these authorsargue that challenging demands—such as work pressure—enhance employee well-beingand performance, whereas hindering demands—such as role conflict—decrease well-beingand performance, in line with the “health impairment path” of the JD-R model [18,44].

Second, employees in the high well-being/top performance profile appear to havemore role clarity, performance feedback, work pressure, and role conflict, in comparisonwith employees in the “high well-being/high performance” profile (30.4%). While it isconsistent with the literature that a higher level of job resources and work pressure can leadto more favorable performance outcomes [18,44], it is surprising that those with more roleconflict are also more likely to be in the top performance profile. However, as employeeswithin the top-performance profile also had more role clarity, it may be that role conflictis considered to be inherent to their job and is, therefore, not seen as a negative, as isnormally the case for hindering demands, according to Crawford, LePine, and Rich [44].Consequently, employees who expect role conflict due to their role clarity may have their

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expectations fulfilled by perceiving role conflict in practice, which would be in line withthe finding of Ayala, Silla, Tordera, Lorente, and Yeves [10] that happy-productive employ-ees perceive their psychological contract to be fulfilled, whereas unhappy-unproductiveemployees do not. Consequently, future research could investigate by whom role conflictis perceived as a hindering demand and by whom as challenging demand.

Third, a closer look at the different qualitative profiles suggests two possible expla-nations for membership of the “low well-being/medium performance” profile. Specifi-cally, compared to the “low well-being/low performance” profile, employees in the “lowwell-being/medium performance” profile appeared to have more learning opportunities,communication, role clarity, and work pressure and less support from their managers. Onone hand, and in line with the JD-R model, it may thus be argued that because these specificresources primarily relate to the task level and there is higher work pressure, employeesmay be more enabled and motivated to perform [24,44]. On the other hand, as relationshipwith one’s supervisor has appeared to be related to well-being [54], it may be that therelatively low social support from the manager for this group—compared to the levelsof the “low well-being/low performance” and “high well-being/medium performance”profiles—may lead to its low well-being. Alternatively, the manager may be more likelyto provide social support to employees who have lower well-being scores when they alsohave lower job performance.

Finally, for the other qualitatively different profile (“high well-being/medium perfor-mance”), it appeared that having more work-pressure increased the likelihood of beingin the “high well-being/high performance” and “high well-being/top performance” pro-files, compared to “high well-being/medium performance.” This is consistent with thenotion of Crawford, LePine, and Rich [44] that challenging demands can be motivating.Furthermore, this finding may hint at the existence of the boosting effect described ona few occasions in the literature [55]. The boosting effect suggests that the positive ef-fects of job resources on engagement can be strengthened by certain job demands [25],such as work pressure. This is conceptually different from the buffering effect, whichsuggests that job resources may compensate for high job demands [55]. As interactioneffects between job demands and resources have only been found on a few occasions [24],more research is required to explore whether such interactions may predict employeewell-being/performance profile membership.

4.1. Limitations and Directions for Future Research

Several limitations for our research should be considered. First, due to data privacyand ethical considerations, we only had access to an anonymous dataset, comprising surveydata gathered at one point in time. This means that we were unable to enrich our data withdata from other sources such as the supervisor or human resources (HR) system. Therefore,our outcomes may have been subject to common method bias [56]. To reduce the potentialfor this issue in the future, we recommend that researchers combine data sources. Forinstance, performance ratings from the manager and other well-being indicators, suchas absenteeism data, could be included in the LCA. Furthermore, to assess whether jobdemands and resources lead to certain profiles—or if it is the other way around—and toinvestigate the dynamic nature of these profiles in more detail, we recommend longitudinal(intervention) studies.

Second, the research was conducted with a single company (albeit a large one) thatoperates at the national and international levels, and it included a sample of all staff,from low-ranking up to high-ranking workers in a variety of functions (such as sales,accounting, compliance, stockbroking, and HR), each based in the company’s Dutchdivision or head office. This one company provides an example of the possibilities of usingLCA to distinguish the different profiles/categories of staff with respect to the balance orimbalance between well-being and performance and related job resources and demands.Owing to the increasing availability of survey-based data in large companies [57], other

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firms could conduct similar analyses to discern the profiles found in their own organizationsand make improvements to the mixes of available resources and challenging demands.

Third, in this research, no interaction effects between job demands and resources wereused to predict membership of the well-being and performance profiles. We made thischoice because of the limited empirical evidence [24] and theoretical consensus on theseeffects. For instance, whereas the JD-R model has traditionally only focused on bufferingeffects in which job resources can compensate for high demands, boosting effects haverecently been introduced to explain how job demands can strengthen the positive effectsof job resources [58]. Furthermore, although some researchers have suggested that jobresources should be matched to job demands to find significant interaction effects, othershave shown that job demands and resources that have conceptually little in common areable to boost engagement [55]. Considering that well-being and performance profiles arerelatively underexplored, we made the choice not to further complicate our research byalso investigating these interactions. Nevertheless, some of our findings do hint to theexistence of interaction effects, such as the “high well-being/top performance” profilesexperiencing both role clarity and role conflict. Therefore, in line with other scholars [59],we recommend that future researchers explore these possible interactions between jobdemands and resources.

Finally, as the well-being and performance profiles that we found in this study reflectpatterns of realized outcomes of organizations across (some of the) financial and socialgoals, future research could also use the distribution of these profiles as organizational-level proxies for the intensity with which organizations engage in corporate sustainability.In addition, the distribution of well-being and performance profiles could serve as anindicator or outcome of the organization’s perspective on the employment relationship.Using the profiles, scholars could, for example, identify the proportion of individuals in anorganization who have sustainable work (those belonging to the “high well-being/highperformance” profile) and investigate whether these organizations are more likely to adopta mutual investment approach. A company with a mutual investment approach is onethat typically invests heavily in its employees (e.g., providing training and developmentopportunities) and expects high employee contributions in return [60].

4.2. Practical Implications

This research has several practical implications. First, a little over half of our sampleprovide support for the hypothesis that employees who feel well also perform well e.g., [61].However, 41.2% of our sample was in one of the two profiles characterized by a trade-offbetween well-being and performance. This means that managers—preferably in closecooperation with their HR business manager—should consider these complex employeewell-being/performance patterns when deciding upon an intervention. For example, toincrease well-being and performance, managers should increase the job resources to whichan employee has access if the individual is in the “low well-being/low performance” profile.These valuable job resources include learning and development, sufficient autonomy, andproper performance feedback. However, to increase the performance of the “high well-being/medium performance” profile, work pressure should be increased. The latter is inline with the Crawford, LePine, and Rich [44] assumption that challenging demands maymotivate employees to perform.

Second, the largest single profile (30.4%) is the “high well-being/high performance”group, while relatively few are in the “low well-being/low performance” (12.1%) or“high well-being/top performance” (16.2%) profiles. This shows that situations in whichemployees have very limited resources and high hindering demands (“low well-being/lowperformance”) are relatively rare and there is more to gain by further improving theperformance levels of the “high well-being/high performance” group. Based upon ourresearch, we suggest that managers could achieve this by enhancing the role clarity andfeedback of employees in the “high well-being/high performance group.” This could bedone, for example, by being particularly attentive to role clarity when employees are new

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to their job during the socialization phase [62]. Alternatively, a manager may also work onhis or her own style of feedback, making sure not to fall into the trap of micromanagementand instead focusing on the targets to be achieved and facilitating conditions (such as roleclarity). This should ensure that employees are more receptive to feedback e.g., [63].

5. Conclusions

In pursuit of economic viability and social responsibility, many organizations striveto create jobs that increase employee well-being and organizational performance. Takinga person-centered approach, we identified five employee energy-related well-being andperformance profiles, of which some have trade-offs (e.g., “low well-being/medium per-formance” and “high well-being/medium performance”). As each profile appears to bepredicted by distinct job demands and resources, this study sheds light on the difficult taskof creating jobs that are beneficial to both organizations and their employees.

Author Contributions: All authors substantially contributed to the conceptualization, methodol-ogy, validation, formal analysis, investigation, resources, data curation, writing—original draftpreparation, writing—review and editing, visualization, supervision, and project administrationof the research and research article. All authors have read and agreed to the published version ofthe manuscript.

Funding: This research received no external funding.

Institutional Review Board Statement: The study was conducted according to the guidelines of theDeclaration of Helsinki and approved by the Ethics Review Board of Tilburg University. Permissionto use the third-party data for academic research was granted by the data privacy officer and themanagement of the company.

Informed Consent Statement: The organization central to this research obtained informed consentfrom all subjects involved in the study.

Data Availability Statement: Data is owned by a third-party company and is not publicly availabledue to the sensitive and confidential nature of the data.

Conflicts of Interest: The authors declare no conflict of interest.

References1. Battilana, J.; Obloj, T.; Pache, A.-C.; Sengul, M. Beyond Shareholder Value Maximization: Accounting for Financial/Social

Tradeoffs in Dual-Purpose Companies. Acad. Manag. Rev. 2020, 6, 18–50. [CrossRef]2. Jackson, S.E.; Schuler, R.S.; Jiang, K. An aspirational framework for strategic human resource management. Acad. Manag. Ann.

2014, 8, 1–56. [CrossRef]3. Boxall, P. Mutuality in the management of human resources: Assessing the quality of alignment in employment relationships.

Hum. Resour. Manag. J. 2013, 23, 3–17. [CrossRef]4. Boxall, P. Studying mutuality and perversity in the impacts of human resource management on societal well-being: Advancing a

pluralist agenda. Hum. Resour. Manag. J. 2021. [CrossRef]5. Beer, M.; Boselie, P.; Brewster, C. Back to the future: Implications for the field of HRM of the multistakeholder perspective

proposed 30 years ago. Hum. Resour. Manag. 2015, 54, 427–438. [CrossRef]6. Jiang, K.; Lepak, D.P.; Han, K.; Hong, Y.; Kim, A.; Winkler, A.-L. Clarifying the construct of human resource systems: Relating

human resource management to employee performance. Hum. Resour. Manag. Rev. 2012, 22, 73–85. [CrossRef]7. Van De Voorde, K.; Paauwe, J.; Van Veldhoven, M. Employee well-being and the HRM–organizational performance relationship:

A review of quantitative studies. Int. J. Manag. Rev. 2012, 14, 391–407. [CrossRef]8. Ogbonnaya, C.N.; Nielsen, K. Transformational leadership, high performance work practices, and an effective organization. In

Proceedings of the 76th Annual Meeting of the Academy of Management, Anaheim, CA, USA, 5–9 August 2016.9. Peccei, R.; Van De Voorde, K. Human resource management–well-being–performance research revisited: Past, present, and future.

Hum. Resour. Manag. J. 2019, 29, 539–563. [CrossRef]10. Ayala, Y.; Silla, J.M.P.; Tordera, N.; Lorente, L.; Yeves, J. Job satisfaction and innovative performance in young spanish employees:

Testing new patterns in the happy-productive worker thesis—A discriminant study. J. Happiness Stud. 2017, 18, 1377–1401.[CrossRef]

11. Peiró, J.M.; Kozusznik, M.W.; Rodríguez-Molina, I.; Tordera, N. The happy-productive worker model and beyond: Patterns ofwellbeing and performance at work. Int. J. Environ. Res. Public Health 2019, 16, 479. [CrossRef]

Sustainability 2021, 13, 7424 18 of 19

12. Tordera, N.; Peiro, J.M.; Ayala, Y.; Villajos, E.; Truxillo, D. The lagged influence of organizations’ human resources practices onemployees’ career sustainability: The moderating role of age. J. Vocat. Behav. 2020, 120, 103444. [CrossRef]

13. Benitez, M.; Peccei, R.; Medina, F.J. Employee well-being profiles and service quality: A unit-level analysis using a multilevellatent profile approach. Eur. J. Work Organ. Psychol. 2019, 28, 859–872. [CrossRef]

14. Salanova, M.; Del Líbano, M.; Llorens, S.; Schaufeli, W.B. Engaged, workaholic, burned-out or just 9-to-5? Toward a typology ofemployee well-being. Stress Health 2014, 30, 71–81. [CrossRef] [PubMed]

15. Somers, M.; Birnbaum, D.; Casal, J. Application of the person-centered model to stress and well-being research. Empl. Relat. Int. J.2019, 41, 649–661. [CrossRef]

16. LePine, J.A.; Podsakoff, N.P.; LePine, M.A. A meta-analytic test of the challenge stressor–hindrance stressor framework: Anexplanation for inconsistent relationships among stressors and performance. Acad. Manag. J. 2005, 48, 764–775. [CrossRef]

17. Podsakoff, N.P.; LePine, J.A.; LePine, M.A. Differential challenge stressor-hindrance stressor relationships with job attitudes,turnover intentions, turnover, and withdrawal behavior: A meta-analysis. J. Appl. Psychol. 2007, 92, 438. [CrossRef]

18. Schaufeli, W.B.; Bakker, A.B. Job demands, job resources, and their relationship with burnout and engagement: A multi-samplestudy. J. Organ. Behav. Int. J. Ind. Occup. Organ. Psychol. Behav. 2004, 25, 293–315. [CrossRef]

19. Eurofound. Sixth European Working Conditions Survey–Overview Report (2017 Update); Publications Office of the European Union:Luxembourg, 2017.

20. Campbell, J.P.; Wiernik, B.M. The modeling and assessment of work performance. Annu. Rev. Organ. Psychol. Organ. Behav. 2015,2, 47–74. [CrossRef]

21. Erdogan, B.; Bauer, T.N.; Truxillo, D.M.; Mansfield, L.R. Whistle While You Work: A Review of the Life Satisfaction Literature. J.Manag. 2012, 38, 1038–1083. [CrossRef]

22. Harter, J.K.; Schmidt, F.L.; Hayes, T.L. Business-unit-level relationship between employee satisfaction, employee engagement,and business outcomes: A meta-analysis. J. Appl. Psychol. 2002, 87, 268–279. [CrossRef]

23. Demerouti, E.; Bakker, A.B.; Nachreiner, F.; Schaufeli, W.B. The job demands-resources model of burnout. J. Appl. Psychol. 2001,86, 499. [CrossRef]

24. Bakker, A.B.; Demerouti, E. The job demands-resources model: State of the art. J. Manag. Psychol. 2007, 22, 309–328. [CrossRef]25. Schaufeli, W.B.; Taris, T.W. A critical review of the job demands-resources model: Implications for improving work and health. In

Bridging Occupational, Organizational and Public Health; Springer: Dordrecht, The Netherlands, 2014; pp. 43–68. [CrossRef]26. Campbell, J.P. Modeling the performance prediction problem in industrial and organizational psychology. In Handbook of Industrial

and Organizational Psychology; Consulting Psychologists Press: London, UK, 1990.27. Koopmans, L.; Bernaards, C.M.; Hildebrandt, V.H.; Schaufeli, W.B.; de Vet Henrica, C.; van der Beek, A.J. Conceptual frameworks

of individual work performance: A systematic review. J. Occup. Environ. Med. 2011, 53, 856–866. [CrossRef] [PubMed]28. Schaufeli, W.B.; Salanova, M.; González-Romá, V.; Bakker, A.B. The measurement of engagement and burnout: A two sample

confirmatory factor analytic approach. J. Happiness Stud. 2002, 3, 71–92. [CrossRef]29. Maslach, C.; Jackson, S.E.; Leiter, M.P.; Schaufeli, W.B.; Schwab, R.L. Maslach Burnout Inventory; Consulting Psychologists Press:

Palo Alto, CA, USA, 1986; Volume 21.30. Hofmans, J.; Wille, B.; Schreurs, B. Person-centered methods in vocational research. J. Vocat. Behav. 2020, 118, 103398. [CrossRef]31. Bakker, A.B.; Oerlemans, W. Subjective well-being in organizations. Oxf. Handb. Posit. Organ. Scholarsh. 2011, 49, 178–189.

[CrossRef]32. Hülsheger, U.R.; Lang, J.W.B.; Maier, G.W. Emotional labor, strain and performance: Testing reciprocal relationships in a

longitudinal panel study. J. Occup. Health Psychol. 2010, 15, 505–521. [CrossRef]33. Jiang, W.; Xiao, Z.; Liu, Y.; Guo, K.; Jiang, J.; Du, X. Reciprocal relations between grit and academic achievement: A longitudinal

study. Learn. Individ. Differ. 2019, 71, 13–22. [CrossRef]34. Marsh, H.W.; Lüdtke, O.; Trautwein, U.; Morin, A.J. Classical latent profile analysis of academic self-concept dimensions: Synergy

of person-and variable-centered approaches to theoretical models of self-concept. Struct. Equ. Model. A Multidiscip. J. 2009, 16,191–225. [CrossRef]

35. Meyer, J.P.; Stanley, L.J.; Vandenberg, R.J. A person-centered approach to the study of commitment. Hum. Resour. Manag. Rev.2013, 23, 190–202. [CrossRef]

36. Bennett, A.A.; Gabriel, A.S.; Calderwood, C.; Dahling, J.J.; Trougakos, J.P. Better together? Examining profiles of employeerecovery experiences. J. Appl. Psychol. 2016, 101, 1635. [CrossRef] [PubMed]

37. Gabriel, A.S.; Calderwood, C.; Bennett, A.A.; Wong, E.M.; Dahling, J.J.; Trougakos, J.P. Examining recovery experiences amongworking college students: A person-centered study. J. Vocat. Behav. 2019, 115, 103329. [CrossRef]

38. Meijman, T.; Mulder, G.; Drenth, P.; Thierry, H.; de Wolff, C. Handbook of work and organizational psychology. Work Psychol.1998, 2, 5–33.

39. Ryan, R.M.; Deci, E.L. Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being.Am. Psychol. 2000, 55, 68. [CrossRef] [PubMed]

40. Xanthopoulou, D.; Baker, A.B.; Heuven, E.; Demerouti, E.; Schaufeli, W.B. Working in the sky: A diary study on work engagementamong flight attendants. J. Occup. Health Psychol. 2008, 13, 345. [CrossRef] [PubMed]

Sustainability 2021, 13, 7424 19 of 19

41. Hobfoll, S.E.; Freedy, J. Conservation of resources: A general stress theory applied to burnout. In Professional Burnout: RecentDevelopments in Theory and Research; Series in applied psychology: Social issues and questions; Taylor & Francis: Philadelphia, PA,USA, 1993; pp. 115–133.

42. Lee, R.T.; Ashforth, B.E. A meta-analytic examination of the correlates of the three dimensions of job burnout. J. Appl. Psychol.1996, 81, 123. [CrossRef]

43. Hockey, G.R.J. Compensatory control in the regulation of human performance under stress and high workload: A cognitive-energetical framework. Biol. Psychol. 1997, 45, 73–93. [CrossRef]

44. Crawford, E.R.; LePine, J.A.; Rich, B.L. Linking job demands and resources to employee engagement and burnout: A theoreticalextension and meta-analytic test. J. Appl. Psychol. 2010, 95, 834. [CrossRef]

45. Tubre, T.C.; Collins, J.M. Jackson and Schuler (1985) revisited: A meta-analysis of the relationships between role ambiguity, roleconflict, and job performance. J. Manag. 2000, 26, 155–169. [CrossRef]

46. Humphrey, S.E.; Nahrgang, J.D.; Morgeson, F.P. Integrating motivational, social, and contextual work design features: Ameta-analytic summary and theoretical extension of the work design literature. J. Appl. Psychol. 2007, 92, 1332. [CrossRef]

47. Brauner, C.; Wöhrmann, A.M.; Frank, K.; Michel, A. Health and work-life balance across types of work schedules: A latent classanalysis. Appl. Ergon. 2019, 81, 102906. [CrossRef]

48. Van Den Groenendaal, S.M.E.; Rossetti, S.; Van Den Bergh, M.; Kooij, T.D.; Poell, R.F. Motivational profiles and proactive careerbehaviors among the solo self-employed. Career Dev. Int. 2021, 26, 309–330. [CrossRef]

49. Magidson, J.; Vermunt, J.K.; Madura, J.P. Latent Class Analysis; SAGE Publications Limited: London, UK, 2020.50. Schaufeli, W.B.; Bakker, A.B.; Salanova, M. The measurement of work engagement with a short questionnaire: A cross-national

study. Educ. Psychol. Meas. 2006, 66, 701–716. [CrossRef]51. Schaufeli, W.B.; Van Dierendonck, D. Handleiding van de Utrechtse Burnout Schaal (UBOS) [Manual Utrecht Burnout Scale]; Swets

Test Services: Lisse, The Netherlands, 2000; pp. 177–196.52. Vermunt, J.K.; Magidson, J. Latent class analysis. Sage Encycl. Soc. Sci. Res. Methods 2004, 2, 549–553.53. Morin, A.J.; Meyer, J.P.; Creusier, J.; Biétry, F. Multiple-group analysis of similarity in latent profile solutions. Organ. Res. Methods

2016, 19, 231–254. [CrossRef]54. Hooper, D.T.; Martin, R. Beyond personal leader–member exchange (LMX) quality: The effects of perceived LMX variability on

employee reactions. Leadersh. Q. 2008, 19, 20–30. [CrossRef]55. Bakker, A.B.; Hakanen, J.J.; Demerouti, E.; Xanthopoulou, D. Job resources boost work engagement, particularly when job

demands are high. J. Educ. Psychol. 2007, 99, 274–284. [CrossRef]56. Podsakoff, P.M.; MacKenzie, S.B.; Lee, J.-Y.; Podsakoff, N.P. Common method biases in behavioral research: A critical review of

the literature and recommended remedies. J. Appl. Psychol. 2003, 88, 879. [CrossRef]57. Levenson, A.; Fink, A. Human capital analytics: Too much data and analysis, not enough models and business insights. J. Organ.

Eff. People Perform. 2017, 4, 145–156. [CrossRef]58. Bakker, A.B.; Demerouti, E. Job demands–resources theory: Taking stock and looking forward. J. Occup. Health Psychol. 2017, 22,

273–285. [CrossRef] [PubMed]59. Van Veldhoven, M.; Van den Broeck, A.; Daniels, K.; Bakker, A.B.; Tavares, S.M.; Ogbonnaya, C. Challenging the universality of

job resources: Why, when, and for whom are they beneficial? Appl. Psychol. 2020, 69, 5–29. [CrossRef]60. Tsui, A.S.; Pearce, J.L.; Porter, L.W.; Tripoli, A.M. Alternative approaches to the employee-organization relationship: Does

investment in employees pay off? Acad. Manag. J. 1997, 40, 1089–1121. [CrossRef]61. Wright, T.A.; Cropanzano, R.; Bonett, D.G. The moderating role of employee positive well being on the relation between job

satisfaction and job performance. J. Occup. Health Psychol. 2007, 12, 93. [CrossRef] [PubMed]62. Frögéli, E.; Rudman, A.; Gustavsson, P. The relationship between task mastery, role clarity, social acceptance, and stress: An

intensive longitudinal study with a sample of newly registered nurses. Int. J. Nurs. Stud. 2019, 91, 60–69. [CrossRef] [PubMed]63. Steelman, L.A.; Wolfeld, L. The manager as coach: The role of feedback orientation. J. Bus. Psychol. 2018, 33, 41–53. [CrossRef]